Governance Is a Differentiator: Safety, Regulation, and Incident Readiness for AI Products
Layer 4 of AI Fluency: Why safety and governance determine whether customers stay after an incident
#AI Fluency Series.
6 posts
Triage: The Diagnostic Discipline That Separates Fixable From Fundamental
Layer 3 of the AI Fluency framework, Part 2: the diagnostic discipline. When your AI product breaks, how to figure out whether the model or your application is at fault, route the fix to the right team, and give stakeholders a credible timeline.
Context Engineering: What Comes After Prompt Engineering
Layer 3 of the AI Fluency framework, Part 1: designing the application layer. The quality ceiling of your AI product is set by what reaches the model, not how you phrase the question. Learn context engineering: the five types of context, retrieval techniques, and assembly decisions that are product decisions, not engineering details.
The Eval Gap: Why Most AI Products Ship Blind
Why evaluation is the biggest genuine gap most product managers have. Teams ship AI features without knowing whether they work, discover regressions from customer complaints, and can't measure impact. This is Layer 2 of the AI Fluency framework.
What Product Managers Actually Need to Know About How Models Work
Layer 1 of the AI Fluency framework: enough technical knowledge to hold your own in conversations with engineers, without becoming an ML researcher.
The Four-Layer Model: A PM's Framework for AI Product Quality
A structured framework for understanding and diagnosing AI product failures. Learn the four layers of technical literacy PMs need, and how real incidents cross all of them at once.